Satellite signal based positioning technologies, which are mainly used outdoors, are usually not suited to deliver a satisfactory performance when used for indoor positioning, since satellite signals of global navigation satellite systems (GNSS), like the global positioning system (GPS), do not penetrate through walls and roofs strongly enough for an adequate signal reception indoors. Thus, these positioning technologies are not able to deliver a performance indoors that would enable seamless, equal and accurate navigation experience outdoors and indoors.
Therefore, several dedicated solutions for indoor positioning have been developed and commercially deployed during the past years. Examples comprise solutions that are based on pseudolites, which are ground based GPS-like short-range beacons, ultra-sound positioning solutions, Bluetooth low energy (BTLE) based positioning solutions, and wireless local area network (WLAN) based positioning solutions.
Particularly in case signals of opportunity are used for positioning, i.e. signals which primarily server another purpose than positioning, e.g. communication and data transmission, as it is the case with WLAN signals, for example, the positioning accuracy may be not sufficiently precise. Additionally, in case of a WLAN based positioning solution, for instance, the process generally has to be divided in two stages, a training stage and a positioning stage.
In the training stage, learning data is collected. The data may be collected in the form of fingerprints that are based on measurements by mobile devices. A fingerprint may contain a location estimate and measurements taken from the radio interface. The location estimate may be for example GNSS based, sensor-based, or manually inputted. Measurements taken from the radio interface may comprise, by way of example, measured radio signal strengths and an identification of WLAN access points transmitting the radio signals. The training may be a continuous background process, in which mobile devices of a large number of consumers are continuously reporting measured data to a server. Consumers may consent to a participation in such a data collection, if their device is equipped with the needed functionality. This approach is also referred to as crowd-sourcing. Alternatively or in addition, mobile devices may be used for collecting fingerprints in a systematic manner. Collected fingerprint data may be uploaded to a database in a server or in the cloud, where algorithms may be run to generate models of WLAN access points for positioning purposes.
In the positioning stage, a mobile device may estimate its current location based on own measurements taken from the radio interface and on the data or a subset of data that is available from the training stage. Models or parts of models that have been generated in the training stage may be transferred to mobile devices for use in position determination. Alternatively, the models may be stored in a positioning server to which the mobile devices may connect to for obtaining position information.
A similar approach could be used for a positioning that is based on other types of terrestrial communication nodes or on a combination of different types of terrestrial communication nodes.
While the advantage of this approach is the rather easy setup of the venue environment, as it can in particular utilize deployed existing hardware, such as wireless access points or installed Bluetooth beacons. However, these indoor solutions require manual exhaustive radio-surveying of the buildings including all the floors, spaces and rooms. This is rather expensive and will take a considerable amount of time to build the coverage to the commercially expected level, which in some cases narrowed the potential market segment only to very thin customer base. Also, the diversity of these technologies makes it difficult to build a globally scalable indoor positioning solution, and the integration and testing will become complex if a large number of technologies is needed to be supported in the consumer mobile devices, such as smartphones.
Further, the quality of obtained positioning information may vary in particular based on the efforts put into the training stage or on the up-to-dateness of the collecting data. Additionally, one has to consider the fact, that many mobile devices' wireless (e.g. WLAN or Bluetooth) adapter are originally not designed to provide accurate measures e.g. of the signal strength with fine granularity to user applications. Thus, the final accuracy obtained from such an approach has an error that can range from one to even a few meters.
Therefore, important factors to consider for a successful indoor positioning solution may include:                the accuracy of the determined position,        the independence from additional hardware accessories at the mobile device,        an acceptable effort for deploying any infrastructure,        a minimal distortion or disruption of the environment by the setup of the infrastructure.        